Motion vector refinement methods for video encoding

ABSTRACT

A method, computer program, and computer system is provided for video coding. Video data comprising one or more blocks is received. One or more samples are identified from one or more references images corresponding to the received video data. One or more motion vectors associated with the video data are refined based on interpolating the one or more samples by applying a decoder-side motion vector refinement (DMVR) motion vector offset value to a motion vector associated with a current block from among the one or more blocks.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority based on U.S. Provisional ApplicationNo. 62/895,268 (filed Sep. 3, 2019) and U.S. Provisional Application No.62/903,866 (filed Sep. 22, 2019), the entirety of which are incorporatedherein.

FIELD

This disclosure relates generally to field of data processing, and moreparticularly to video coding.

BACKGROUND

ITU-T VCEG (Q6/16) and ISO/IEC MPEG (JTC 1/SC 29/WG 11) published theH.265/HEVC (High Efficiency Video Coding) standard in 2013 (version 1)2014 (version 2) 2015 (version 3) and 2016 (version 4). Since then, thepotential need for standardization of future video coding technologywhich could significantly outperform HEVC in compression capability hasbeen studied. In October 2017, a Joint Call for Proposals on VideoCompression with Capability beyond HEVC (CfP) was issued. By Feb. 15,2018, total 22 CfP responses on standard dynamic range (SDR), 12 CfPresponses on high dynamic range (HDR), and 12 CfP responses on 360 videocategories were submitted, respectively. In April 2018, all received CfPresponses were evaluated in the 122 MPEG/10^(th) JVET (Joint VideoExploration Team-Joint Video Expert Team) meeting. With carefulevaluation, JVET formally launched the standardization ofnext-generation video coding beyond HEVC, i.e., the so-called VersatileVideo Coding (VVC).

SUMMARY

Embodiments relate to a method, system, and computer readable medium forvideo coding. According to one aspect, a method for video coding isprovided. The method may include receiving video data comprising one ormore blocks. One or more samples are identified from one or morereferences images corresponding to the received video data. One or moremotion vectors associated with the video data are refined based oninterpolating the one or more samples.

According to another aspect, a computer system for video coding isprovided. The computer system may include one or more processors, one ormore computer-readable memories, one or more computer-readable tangiblestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories, wherebythe computer system is capable of performing a method. The method mayinclude receiving video data comprising one or more blocks. One or moresamples are identified from one or more references images correspondingto the received video data. One or more motion vectors associated withthe video data are refined based on interpolating the one or moresamples.

According to yet another aspect, a computer readable medium for videocoding is provided. The computer readable medium may include one or morecomputer-readable storage devices and program instructions stored on atleast one of the one or more tangible storage devices, the programinstructions executable by a processor. The program instructions areexecutable by a processor for performing a method that may accordinglyinclude receiving video data comprising one or more blocks. One or moresamples are identified from one or more references images correspondingto the received video data. One or more motion vectors associated withthe video data are refined based on interpolating the one or moresamples.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is a block diagram of a system for video coding, according to atleast one embodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program for video coding, according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to media processing. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, encode and/or decode video using decoder side motionvector refinement based on samples associated with reference images.Therefore, some embodiments have the capacity to improve the field ofcomputing by using the actual samples loaded from the references imagesinstead of padding to save on memory usage.

As previously described, ITU-T VCEG (Q6/16) and ISO/IEC MPEG (JTC 1/SC29/WG 11) published the H.265/HEVC (High Efficiency Video Coding)standard in 2013 (version 1) 2014 (version 2) 2015 (version 3) and 2016(version 4). Since then, the potential need for standardization offuture video coding technology which could significantly outperform HEVCin compression capability has been studied. In October 2017, a JointCall for Proposals on Video Compression with Capability beyond HEVC(CfP) was issued. By Feb. 15, 2018, total 22 CfP responses on standarddynamic range (SDR), 12 CfP responses on high dynamic range (HDR), and12 CfP responses on 360 video categories were submitted, respectively.In April 2018, all received CfP responses were evaluated in the 122MPEG/10^(th) JVET (Joint Video Exploration Team-Joint Video Expert Team)meeting. With careful evaluation, JVET formally launched thestandardization of next-generation video coding beyond HEVC, i.e., theso-called Versatile Video Coding (VVC).

In VVC, video data can be broken down into one or more blocks. When ablock applies decoder side motion vector refinement (DMVR), one or morereference samples may be padded. The reference samples may not be neededfor the interpolation process if the motion compensation is based on theoriginal motion vector, but the reference samples may be needed for theinterpolation process if the motion compensation is based on the refinedmotion vector. However, the padding process may need additional memoryallocation to avoid overwriting the reference samples. It may beadvantageous, therefore, to use the actual samples loaded from thereferences images instead of padding. The samples may be loaded fromreference pictures for the interpolation process in the extended regionswhen a DMVR offset may be applied. In this way, the padding in theextended area for DMVR may no longer be necessary.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that allows for video coding using reference imagesamples without padding. Referring now to FIG. 1, a functional blockdiagram of a networked computer environment illustrating a mediaprocessing system 100 (hereinafter “system”) for video coding. It shouldbe appreciated that FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (IaaS), as discussed below withrespect to FIGS. 5 and 6. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for video coding is enabledto run an DMVR Video Coding Program 116 (hereinafter “program”) that mayinteract with a database 112. The DMVR Video Coding Program method isexplained in more detail below with respect to FIG. 3. In oneembodiment, the computer 102 may operate as an input device including auser interface while the program 116 may run primarily on servercomputer 114. In an alternative embodiment, the program 116 may runprimarily on one or more computers 102 while the server computer 114 maybe used for processing and storage of data used by the program 116. Itshould be noted that the program 116 may be a standalone program or maybe integrated into a larger DMVR video coding program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2, a block diagram of an exemplary DMVR videocoding system 200 is depicted. The DMVR video coding system 200 mayinclude a motion vector refinement module 202. The motion vectorrefinement module 202 may receive video data 204 and reference imagedata 206 as inputs. The motion vector refinement module 202 may refinemotion vectors of the video data 204 using the reference image data 206.The motion vector refinement module 202 may output video data 208.

In one or more embodiments, the actual reference samples are used ininterpolation process for motion compensation. The refined motion vectormay be is generated by applying a DMVR motion vector offset value to theinitial motion vector of the current block. No padding may be used insuch cases, and the search range of DMVR search may be dependent on thesize of the block for which DMVR is applied. Therefore, for differentblock sizes, the search range for DMVR operation may be different.

For example, let SW and SH denote the search range of DMVR on horizontaland vertical directions, respectively and let W and H denote the widthand height of current block in luma samples, respectively. It may beassumed that the interpolation filter may be an N-tap filter. For DMVR,the memory bandwidth requirement for the W*H block is(W+N−1+2*SW)*(H+N−1+2*SH). For W=16 and H=16, N=8 (i.e., an 8-tapinterpolation filter), the search range SW and SH may be both equal to2. The memory bandwidth may be (16+8−1+2*2)*(16+8−1+2*2)/(16*16)*2=5.70,which may be below a worst case of memory bandwidth in VTM-6, where 8×8bi-prediction is the worst case, i.e., (8+8−1)*(8+8−1)/(8*8)*2=7.03.

In one or more embodiments, SW and SH may be both equal to 3, which maylead to a memory bandwidth of (16+8−1+2*3)*(16+8−1+2*3)/(16*16)*2=6.57.

Alternatively, SW=4 and SH=3, which may lead to a bandwidth of(16+8−1+2*4)*(16+8−1+2*3)/(16*16)*2=7.02. Where W<16 or H<16, N=8, thesearch range SW and SH may be set to 1, and a worst case memorybandwidth is (8+8-1+2*1)*(16+8−1+2*1)/(8*16)*2=6.64.

In one or more embodiments, SW and SH may be dependent on W and H andmay not be the same. For example, when W=16 and H=8, SW=2 and SH=1. Inanother example, when W=16 and H=8, SW=1 and SH=2.

In one or more embodiments, the DMVR block size may be limited such thatit is allowed to blocks with a minimum length of 16 luma samples on eachside. The actual reference samples may be used by the interpolationprocess using the refined motion vector from DMVR. The expanded area ofreference samples may be loaded with the DMVR search range included. Oneor more DMVR enabling conditions may checked, which may include:

MergeTriangleFlag may equal 0 (i.e., Triangular Prediction Mode may notbe used).

Inter_affine_flag may equal 0 (i.e., inter affine prediction is notused).

Merge_subblock_flag may equal 0 (i.e., sub-block based merge mode maynot be used).

sps_dmvr_enabled_flag may equal 1 and slice_disable_bdof_dmvr_flag mayequal 0.

general_merge_flag may equal 1.

Both predFlagL0 and predFlagL1 may equal 1, which may mean the block maybe coded using bi-prediction mode.

mmvd_flag may equal 0, which indicates MMVD mode may not be used.

BCW weight index may indicates equal weight.

luma_weight_10_flag[refIdxL0] and luma_weight_11_flag[refIdxL1] mayequal 0, which may indicate equal weighting may be used forbi-prediction.

DiffPicOrderCnt(currPic, RefPicList[0][refIdxL0]) may equalDiffPicOrderCnt(RefPicList[1][refIdxL1], currPic), which may meandistances from both reference pictures to the current picture may be thesame.

cbWidth (coding block width) may be greater than or equal to 16.

cbHeight (coding block height) may be greater than or equal to 16.

For X being each of 0 and 1, the pic_width_in_luma_samples andpic_height_in_luma_samples of the reference picture refPicLX associatedwith the refIdxLX may equal the pic_width_in_luma_samples andpic_height_in_luma_samples of the current picture, respectively.

Referring now to FIG. 3, an operational flowchart 300 illustrating thesteps carried out by a program for video coding is depicted. FIG. 3 maybe described with the aid of FIGS. 1 and 2. As previously described, theDMVR Video Coding Program 116 (FIG. 1) may encode and decode video datausing reference image samples without padding.

At 302, video data comprising one or more blocks is received. The videodata may include one or more motion vectors and may be composed of oneor more luma samples in the vertical and horizontal directions. Inoperation, the DMVR Video Coding Program 116 (FIG. 1) on the servercomputer 114 (FIG. 1) may receive video data 204 (FIG. 2) from thecomputer 102 (FIG. 1) over the communication network 110 (FIG. 1) or mayretrieve the video data 204 from the database 112 (FIG. 1).

At 304, one or more samples are identified from one or more referencesimages corresponding to the received video data. The reference imagesmay correspond to, among other things, one or more adjacent frames forinter-frame prediction. The reference images may be composed of one ormore luma samples in the vertical and horizontal directions. Inoperation, the DMVR Video Coding Program 116 (FIG. 1) on the servercomputer 114 (FIG. 1) may receive reference image data 206 (FIG. 2) fromthe computer 102 (FIG. 1) over the communication network 110 (FIG. 1) ormay retrieve the reference image data 206 from the database 112 (FIG.1). The DMVR Video Coding Program 116 may identify one or more samplesfrom the reference image data 206.

At 306, one or more motion vectors associated with the video data arerefined based on interpolating the one or more samples. The one or moremotion vectors may be refined based on applying a decoder-side motionvector refinement (DMVR) motion vector offset value to a motion vectorassociated with a current block from among the one or more blocks. Therefining may be enabled or disabled by the presence of one or moreconditions. In operation, the motion vector refinement module 202 (FIG.2) may interpolate the video data 204 (FIG. 2) using samples from thereference image data 206 (FIG. 2) without padding. The motion vectorrefinement module 202 may output video data 208 (FIG. 2), which may beencoded or decoded with inter-frame prediction.

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 4. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the DMVR Video Coding Program 116 (FIG. 1) on server computer 114(FIG. 1) are stored on one or more of the respective computer-readabletangible storage devices 830 for execution by one or more of therespective processors 820 via one or more of the respective RAMs 822(which typically include cache memory). In the embodiment illustrated inFIG. 4, each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory, anoptical disk, a magneto-optic disk, a solid state disk, a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of non-transitory computer-readabletangible storage device that can store a computer program and digitalinformation.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the DMVR Video Coding Program 116 (FIG. 1) canbe stored on one or more of the respective portable computer-readabletangible storage devices 936, read via the respective R/W drive orinterface 832 and loaded into the respective hard drive 830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and theDMVR Video Coding Program 116 (FIG. 1) on the server computer 114(FIG. 1) can be downloaded to the computer 102 (FIG. 1) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the DMVR Video CodingProgram 116 on the server computer 114 are loaded into the respectivehard drive 830. The network may comprise copper wires, optical fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5, illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6, a set of functional abstraction layers 600 providedby cloud computing environment 500 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and DMVR Video Coding 96. DMVR Video Coding96 may allow for video coding using reference image samples withoutpadding.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of coding video data, executable by aprocessor, comprising: receiving video data comprising one or moreblocks; identifying one or more samples from one or more referencesimages corresponding to the received video data; and refining one ormore motion vectors associated with the video data based oninterpolating the one or more samples.
 2. The method of claim 1, whereinrefining the one or more motion vectors is enabled based at least onefrom among a triangle prediction mode, an inter affine prediction mode,and a sub-block based merge mode not being used.
 3. The method of claim1, wherein refining the one or more motion vectors is enabled based on acoding block width or a coding block height being greater than or equalto 16 pixels.
 4. The method of claim 1, wherein a size associated withthe one or more blocks is limited to a minimum length of 16 lumasamples.
 5. The method of claim 1, wherein the one or more motionvectors are refined based on applying a decoder-side motion vectorrefinement (DMVR) motion vector offset value to a motion vectorassociated with a current block from among the one or more blocks. 6.The method of claim 1, wherein a search range of decoder-side motionvector refinement (DMVR) search is dependent on the size of the blockfor which DMVR is applied.
 7. The method of claim 1, further comprisingencoding or decoding the video based on the refined motion vectors.
 8. Acomputer system for video coding, the computer system comprising: one ormore computer-readable non-transitory storage media configured to storecomputer program code; and one or more computer processors configured toaccess said computer program code and operate as instructed by saidcomputer program code, said computer program code including: receivingcode configured to cause the one or more computer processors to receivevideo data comprising one or more blocks; identifying code configured tocause the one or more computer processors to identify one or moresamples from one or more references images corresponding to the receivedvideo data; and refining code configured to cause the one or morecomputer processors to refine one or more motion vectors associated withthe video data based on interpolating the one or more samples.
 9. Thecomputer system of claim 8, wherein refining the one or more motionvectors is enabled based at least one from among a triangle predictionmode, an inter affine prediction mode, and a sub-block based merge modenot being used.
 10. The computer system of claim 8, wherein refining theone or more motion vectors is enabled based on a coding block width or acoding block height being greater than or equal to 16 pixels.
 11. Thecomputer system of claim 8, wherein a size associated with the one ormore blocks is limited to a minimum length of 16 luma samples.
 12. Thecomputer system of claim 8, wherein the one or more motion vectors arerefined based on applying a decoder-side motion vector refinement (DMVR)motion vector offset value to a motion vector associated with a currentblock from among the one or more blocks.
 13. The computer system ofclaim 8, wherein a search range of decoder-side motion vector refinement(DMVR) search is dependent on the size of the block for which DMVR isapplied.
 14. The computer system of claim 8, further comprising encodingand decoding code configured to cause the one or more computerprocessors to encode or decode the video based on the refined motionvectors.
 15. A non-transitory computer readable medium having storedthereon a computer program for video coding, the computer programconfigured to cause one or more computer processors to: receive videodata comprising one or more blocks; identify one or more samples fromone or more references images corresponding to the received video data;refine one or more motion vectors associated with the video data basedon interpolating the one or more samples.
 16. The computer readablemedium of claim 15, wherein refining the one or more motion vectors isenabled based at least one from among a triangle prediction mode, aninter affine prediction mode, and a sub-block based merge mode not beingused.
 17. The computer readable medium of claim 15, wherein refining theone or more motion vectors is enabled based on a coding block width or acoding block height being greater than or equal to 16 pixels.
 18. Thecomputer readable medium of claim 15, wherein a size associated with theone or more blocks is limited to a minimum length of 16 luma samples.19. The computer readable medium of claim 15, wherein the one or moremotion vectors are refined based on applying a decoder-side motionvector refinement (DMVR) motion vector offset value to a motion vectorassociated with a current block from among the one or more blocks. 20.The computer readable medium of claim 15, wherein the computer programis further configured to cause one or more computer processors to encodeor decode the video based on the refined motion vectors.